setOldClass {methods} | R Documentation

## Specify Names for Old-Style Classes

### Description

Register an old-style (a.k.a. `S3') class as a formally defined class. The `Classes` argument is the character vector used as the `class` attribute; in particular, if there is more than one string, old-style class inheritance is mimiced. Registering via `setOldClass` allows S3 classes to appear as slots or in method signatures.

### Usage

    setOldClass(Classes, where, test = FALSE)

### Arguments

`Classes` | A character vector, giving the names for old-style classes, as they would appear on the right side of an assignment of the `class` attribute.
`where` | Where to store the class definitions, the global or top-level environment by default. (When either function is called in the source for a package, the class definitions will be included in the package's environment by default.)
`test` | flag, if `TRUE`, inheritance must be tested explicitly for each object, needed if the S3 class can have a different set of class strings, with the same first string. See the details below.

### Details

Each of the names will be defined as a virtual class, extending the remaining classes in `Classes`, and the class `oldClass`, which is the “root” of all old-style classes. See Methods for the details of method dispatch and inheritance. See the section Register or Convert? for comments on the alternative of defining “real” S4 classes rather than using `setOldClass`.

S3 classes have no formal definition, and some of them cannot be represented as an ordinary combination of S4 classes and superclasses. It is still possible to register the classes as S4 classes, but now the inheritance has to be verified for each object, and you must call `setOldClass` with argument `test=TRUE`.

For example, ordered factors always have the S3 class `c("ordered", "factor")`. This is proper behavior, and maps simply into two S4 classes, with `"ordered"` extending `"factor"`.

But objects whose class attribute has `"POSIXt"` as the first string may have either (or neither) of `"POSIXct"` or `"POSIXlt"` as the second string. This behavior can be mapped into S4 classes but now to evaluate `is(x, "POSIXlt")`, for example, requires checking the S3 class attribute on each object. Supplying the `test=TRUE` argument to `setOldClass` causes an explicit test to be included in the class definitions. It's never wrong to have this test, but since it adds significant overhead to methods defined for the inherited classes, you should only supply this argument if it's known that object-specific tests are needed.

The list `.OldClassesList` contains the old-style classes that are defined by the methods package. Each element of the list is an old-style list, with multiple character strings if inheritance is included. Each element of the list was passed to `setOldClass` when creating the methods package; therefore, these classes can be used in `setMethod` calls, with the inheritance as implied by the list.

### Register or Convert?

A call to section{setOldClass} creates formal classes corresponding to S3 classes, allows these to be used as slots in other classes or in a signature in `setMethod`, and mimics the S3 inheritance.

However, all such classes are created as virtual classes, meaning that you cannot generally create new objects from the class by calling `new`, and that objects cannot be coerced automatically from or to these classes. All these restrictions just reflect the fact that nothing is inherently known about the “structure” of S3 classes, or whether in fact they define a consistent set of attributes that can be mapped into slots in a formal class definition.

If your class does in fact have a consistent structure, so that every object from the class has the same structure, you may prefer to take some extra time to write down a specific definition in a call to `setClass` to convert the class to a fully functional formal class. On the other hand, if the actual contents of the class vary from one object to another, you may have to redesign most of the software using the class, in which case converting it may not be worth the effort. You should still register the class via `setOldClass`, unless its class attribute is hopelessly unpredictable.

An S3 class has consistent structure if each object has the same set of attributes, both the names and the classes of the attributes being the same for every object in the class. In practice, you can convert classes that are slightly less well behaved. If a few attributes appear in some but not all objects, you can include these optional attributes as slots that always appear in the objects, if you can supply a default value that is equivalent to the attribute being missing. Sometimes `NULL` can be that value: A slot (but not an attribute) can have the value `NULL`. If `version`, for example, was an optional attribute, the old test `is.null(attr(x,"version")` for a missing version attribute could turn into `is.null(x@version)` for the formal class.

The requirement that slots have a fixed class can be satisfied indirectly as well. Slots can be specified with class `"ANY"`, allowing an arbitrary object. However, this eliminates an important benefit of formal class definitions; namely, automatic validation of objects assigned to a slot. If just a few different classes are possible, consider using `setClassUnion` to define valid objects for a slot.

### setOldClass

### See Also

`setClass`, `setMethod`

### Examples

    setOldClass(c("mlm", "lm"))
    setGeneric("dfResidual", function(model)standardGeneric("dfResidual"))
    setMethod("dfResidual", "lm", function(model)model$df.residual)

    ## dfResidual will work on mlm objects as well as lm objects
    myData <- data.frame(time = 1:10, y = (1:10)^.5)
    myLm <- lm(cbind(y, y^3)  ~ time, myData)

    rm(myData, myLm)
    removeGeneric("dfResidual")

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